Enhancing Decision Making Capacity in Tourism Domain Using Social Media Analytics

Abeysinghe, Supun; Manchanayake, Isura; Samarajeewa, Chamod; Rathnayaka, Prabod; Walpola, Malaka J.; Nawaratne, Rashmika; Bandaragoda, Tharindu; Alahakoon, Damminda

doi:10.1109/icter.2018.8615462

Cited by 11 publications

(9 citation statements)

References 27 publications

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“…The information this medium provides about tourist experiences is helpful for tourism operators, destination marketers, and researchers (Park et al, 2018). In 2018, Twitter had around 334 million active users per month (Abeysinghe et al, 2018) who were producing significant content daily through tweets. The volume of tweets makes this form a social media compatible with an automated process of retrieval.…”

Section: Methods Of Data Collectionmentioning

confidence: 99%

“…Further, positive emotional experiences, like feeling happiness or delight, have been linked to positive consumer outcomes, such as, customer satisfaction and future word-of-mouth, in social media (Park et al, 2018). On a larger scale, people's opinion can have a reciprocal relationship with government decisions (Abeysinghe et al, 2018), and this is relevant given the policy choices made by governments to deal with the pandemic, for example, COVID-19 lockdown and closed borders. Thus, identifying emotions in social media postings through an automated approach can make the process easier and offer a deeper understanding of public texts than simple key word matching (Abeysinghe et al, 2018).…”

Section: Emotions and Tourism Destination Imagesmentioning

confidence: 99%

“…In the last few years, emotion detection within publicly posted natural language texts has become a point of interest for researchers (Abeysinghe et al, 2018;Charlton et al, 2016). Sentiment analysis is an established method to measure an opinion leader's positive, negative, or neutral feelings expressed in a group of words relating to an entity (produce or service) or feature of the entity (Chung and Zeng, 2020).…”

Section: Emotions and Tourism Destination Imagesmentioning

confidence: 99%

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Tourism destination image resiliency during a pandemic as portrayed through emotions on Twitter

Nadeau

Wardley

Rajabi

2021

Tourism and Hospitality Research

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The COVID-19 pandemic is having a significant impact on tourism, and emotion projection is one way to understand the extent of destination image resiliency during the crisis. Therefore, this research captured emotions expressed in social media during a peak pandemic month to compare to the prior year period. Toronto and New York were selected due to their tourism importance within their countries but to also compare the effects of different policy approaches used during the pandemic. This study found resiliency of the destination images although there was a significant increase in projections of fear for both cities. Additionally, there was a significant divergence observed for the two cities with a decrease in joy and an increase in sadness projections for New York versus Toronto. This implies that tourism destination marketers have a stable basis of emotions to use in communications, but there are weaknesses to address.

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Section: Methods Of Data Collectionmentioning

confidence: 99%

Section: Emotions and Tourism Destination Imagesmentioning

confidence: 99%

Section: Emotions and Tourism Destination Imagesmentioning

confidence: 99%

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Tourism destination image resiliency during a pandemic as portrayed through emotions on Twitter

Nadeau

Wardley

Rajabi

2021

Tourism and Hospitality Research

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“…Emotional intelligence (or an artificial counterpart) is important for machines when interacting with humans, as emotions and the ability to sense emotion play an important role in maintaining productive social interactions [35]. A significant volume of research has been conducted in the area of detection of emotionally relevant information from different sources, offering positive as well as compelling evidence of impact on multiple fields covering healthcare, human resource management and Artificial Intelligence [1][2][3][4]6]. The traditional…”

Section: Conceptual Backgroundmentioning

confidence: 99%

A voice-based real-time emotion detection technique using recurrent neural network empowered feature modelling

Chamishka

Madhavi

Nawaratne

et al. 2022

Multimed Tools Appl

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The advancements of the Internet of Things (IoT) and voice-based multimedia applications have resulted in the generation of big data consisting of patterns, trends and associations capturing and representing many features of human behaviour. The latent representations of many aspects and the basis of human behaviour is naturally embedded within the expression of emotions found in human speech. This signifies the importance of mining audio data collected from human conversations for extracting human emotion. Ability to capture and represent human emotions will be an important feature in next-generation artificial intelligence, with the expectation of closer interaction with humans. Although the textual representations of human conversations have shown promising results for the extraction of emotions, the acoustic feature-based emotion detection from audio still lags behind in terms of accuracy. This paper proposes a novel approach for feature extraction consisting of Bag-of-Audio-Words (BoAW) based feature embeddings for conversational audio data. A Recurrent Neural Network (RNN) based state-of-the-art emotion detection model is proposed that captures the conversation-context and individual party states when making real-time categorical emotion predictions. The performance of the proposed approach and the model is evaluated using two benchmark datasets along with an empirical evaluation on real-time prediction capability. The proposed approach reported 60.87% weighted accuracy and 60.97% unweighted accuracy for six basic emotions for IEMOCAP dataset, significantly outperforming current state-of-the-art models.

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“…In this approach most frequent words in reviews usually, nouns and pronouns are considered to be candidate of aspects. However, S. Abeysinghe et al [9], then improves the method by applying part-of-speech patterns to filter the terms added to the frequency terms as well. Another approach is using syntactic relations in words to determine the aspects.…”

Section: Literature Reviewmentioning

confidence: 99%

Feature Expansion using Lexical Ontology for Opinion Type Detection in Tourism Reviews Domain

Chen¹,

Gan²

2020

IJACSA

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Tourism reviews platform such as Trip Advisor become a major source for tourists to share their experiences and get some ideas for decision making. Since there are millions of reviews generated daily in the travel websites, tourist is often overwhelmed with huge information. This is where opinion type detection is important as it makes it easy for a tourist to obtain useful reviews for their understanding and planning processes based on the reviews' opinion type. The opinion type of texts in travel mostly involves different aspects of opinion related to the travel process, such as transportation, accommodation, price, food, entertainment, and so on. The challenge of this research is to improve this detection by proposing the lexical ontology approach to address the issue of out-of-vocabulary (OOV) keywords during a supervised detection of opinion type. Besides, there are also issues where the training data for detection has poor coverage or limited in a certain domain. In this paper, we propose a review opinion type detection approach by integrating the word (feature) expansion approach in machine learning. The suggested approach consists of two stages namely feature expansion and classification. For feature expansion, Lexical Ontology (LO) is used to expand the feature-related word to the domains such as synonyms. For classification, the expanded feature is corporate to the Machine Learning approach to detect the opinion type.

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Enhancing Decision Making Capacity in Tourism Domain Using Social Media Analytics

Cited by 11 publications

References 27 publications

Tourism destination image resiliency during a pandemic as portrayed through emotions on Twitter

Tourism destination image resiliency during a pandemic as portrayed through emotions on Twitter

A voice-based real-time emotion detection technique using recurrent neural network empowered feature modelling

Feature Expansion using Lexical Ontology for Opinion Type Detection in Tourism Reviews Domain

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